Applying Batch Normalization in CNN model using TensorFlow
In this section, we have provided a pseudo code, to illustrate how can we apply batch normalization in CNN model using TensorFlow. For applying batch normalization layers after the convolutional layers and before the activation functions, we use âtf.keras.layers.BatchNormalization()â.
import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, BatchNormalization # Build the CNN model model = Sequential([ Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)), BatchNormalization(), # Add batch normalization layer MaxPooling2D((2, 2)), Conv2D(64, (3, 3), activation='relu'), BatchNormalization(), # Add batch normalization layer MaxPooling2D((2, 2)), Flatten(), Dense(64, activation='relu'), Dense(10, activation='softmax') ])
What is Batch Normalization in CNN?
Batch Normalization is a technique used to improve the training and performance of neural networks, particularly CNNs. The article aims to provide an overview of batch normalization in CNNs along with the implementation in PyTorch and TensorFlow.
Table of Content
- Overview of Batch Normalization
- Need for Batch Normalization in CNN model
- How Does Batch Normalization Work in CNN?
- 1. Normalization within Mini-Batch
- 2. Scaling and Shifting
- 3. Learnable Parameters
- 4. Applying Batch Normalization
- 5. Training and Inference
- Applying Batch Normalization in CNN model using TensorFlow
- Applying Batch Normalization in CNN model using PyTorch
- Advantages of Batch Normalization in CNN